{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Retails Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# set the graphs to show in the jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# set seaborn graphs to a better style\n",
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called online_rt\n",
    "Note: if you receive a utf-8 decode error, set `encoding = 'latin1'` in `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv'\n",
    "\n",
    "online_rt = pd.read_csv(path, encoding = 'latin1')\n",
    "\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by the Country\n",
    "countries = online_rt.groupby('Country').sum()\n",
    "\n",
    "# sort the value and get the first 10 after UK\n",
    "countries = countries.sort_values(by = 'Quantity',ascending = False)[1:11]\n",
    "\n",
    "# create the plot\n",
    "countries['Quantity'].plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Quantity')\n",
    "plt.title('10 Countries with most orders')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Exclude negative Quantity entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_rt = online_rt[online_rt.Quantity > 0]\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries (except UK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28c5e47e400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# groupby CustomerID\n",
    "customers = online_rt.groupby(['CustomerID','Country']).sum()\n",
    "\n",
    "# there is an outlier with negative price\n",
    "customers = customers[customers.UnitPrice > 0]\n",
    "\n",
    "# get the value of the index and put in the column Country\n",
    "customers['Country'] = customers.index.get_level_values(1)\n",
    "\n",
    "# top three countries\n",
    "top_countries =  ['Netherlands', 'EIRE', 'Germany']\n",
    "\n",
    "# filter the dataframe to just select ones in the top_countries\n",
    "customers = customers[customers['Country'].isin(top_countries)]\n",
    "\n",
    "#################\n",
    "# Graph Section #\n",
    "#################\n",
    "\n",
    "# creates the FaceGrid\n",
    "g = sns.FacetGrid(customers, col=\"Country\")\n",
    "\n",
    "# map over a make a scatterplot\n",
    "g.map(plt.scatter, \"Quantity\", \"UnitPrice\", alpha=1)\n",
    "\n",
    "# adds legend\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Investigate why the previous results look so uninformative.\n",
    "\n",
    "This section might seem a bit tedious to go through. But I've thought of it as some kind of a simulation of problems one might encounter when dealing with data and other people. Besides there is a prize at the end (i.e. Section 8).\n",
    "\n",
    "(But feel free to jump right ahead into Section 8 if you want; it doesn't require that you finish this section.)\n",
    "\n",
    "#### Step 7.1 Look at the first line of code in Step 6. And try to figure out if it leads to any kind of problem.\n",
    "##### Step 7.1.1 Display the first few rows of that DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>74215</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <th>Iceland</th>\n",
       "      <td>2458</td>\n",
       "      <td>481.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <th>Finland</th>\n",
       "      <td>2341</td>\n",
       "      <td>178.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <th>Italy</th>\n",
       "      <td>631</td>\n",
       "      <td>605.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <th>Norway</th>\n",
       "      <td>197</td>\n",
       "      <td>65.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Quantity  UnitPrice\n",
       "CustomerID Country                            \n",
       "12346.0    United Kingdom     74215       1.04\n",
       "12347.0    Iceland             2458     481.21\n",
       "12348.0    Finland             2341     178.71\n",
       "12349.0    Italy                631     605.10\n",
       "12350.0    Norway               197      65.30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This takes our initial dataframe groups it primarily by 'CustomerID' and secondarily by 'Country'.\n",
    "#It sums all the (non-indexical) columns that have numerical values under each group.\n",
    "customers = online_rt.groupby(['CustomerID','Country']).sum().head()\n",
    "\n",
    "#Here's what it looks like:\n",
    "customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.2 Think about what that piece of code does and display the dtype of `UnitPrice`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.UnitPrice.dtype\n",
    "#So it's 'float64'\n",
    "#But why did we sum 'UnitPrice', to begin with?\n",
    "#If 'UnitPrice' wasn't something that we were interested in then it would be OK\n",
    "#since we wouldn't care whether UnitPrice was being summed or not.\n",
    "#But we want our graphs to reflect 'UnitPrice'!\n",
    "#Note that summing up 'UnitPrice' can be highly misleading.\n",
    "#It doesn't tell us much as to what the customer is doing.\n",
    "#Suppose, a customer places one order of 1000 items that are worth $1 each.\n",
    "#Another customer places a thousand orders of 1 item worth $1.\n",
    "#There isn't much of a difference between what the former and the latter customers did.\n",
    "#After all, they've spent the same amount of money.\n",
    "#so we should be careful when we're summing columns. Sometimes we intend to sum just one column\n",
    "#('Quantity' in this case) and another column like UnitPrice gets ito the mix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.3 Pull data from `online_rt`for `CustomerID`s 12346.0 and 12347.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
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     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428966</th>\n",
       "      <td>573511</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>6</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286637</th>\n",
       "      <td>562032</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>8/2/11 8:48</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72267</th>\n",
       "      <td>542237</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>1/26/11 14:30</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148300</th>\n",
       "      <td>549222</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>4/7/11 10:43</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428967</th>\n",
       "      <td>573511</td>\n",
       "      <td>23173</td>\n",
       "      <td>REGENCY TEAPOT ROSES</td>\n",
       "      <td>2</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>9.95</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode               Description  Quantity  \\\n",
       "428966    573511     22423  REGENCY CAKESTAND 3 TIER         6   \n",
       "286637    562032     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "72267     542237     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "148300    549222     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "428967    573511     23173     REGENCY TEAPOT ROSES          2   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID  Country  \n",
       "428966  10/31/11 12:25      12.75     12347.0  Iceland  \n",
       "286637     8/2/11 8:48      12.75     12347.0  Iceland  \n",
       "72267    1/26/11 14:30      12.75     12347.0  Iceland  \n",
       "148300    4/7/11 10:43      12.75     12347.0  Iceland  \n",
       "428967  10/31/11 12:25       9.95     12347.0  Iceland  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>1/18/11 10:01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "\n",
       "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "61619  1/18/11 10:01       1.04     12346.0  United Kingdom  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(online_rt[online_rt.CustomerID == 12347.0].\n",
    "        sort_values(by='UnitPrice', ascending = False).head())\n",
    "display(online_rt[online_rt.CustomerID == 12346.0].\n",
    "        sort_values(by='UnitPrice', ascending = False).head())\n",
    "#The result is exactly what we'd suspected. Customer 12346.0 placed\n",
    "#one giant order, whereas 12347.0 placed a lot of smaller orders.\n",
    "#So we've identified one potential reason why our plots looked so weird at section 6.\n",
    "#At this stage we need to go back to the initial problem we've specified at section 6.\n",
    "#And make it more precise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.2 Reinterpreting the initial problem.\n",
    "\n",
    "To reiterate the question that we were dealing with:  \n",
    "\"Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries\"\n",
    "\n",
    "The question is open to a set of different interpretations.\n",
    "We need to disambiguate.\n",
    "\n",
    "We could do a single plot by looking at all the data from the top 3 countries.\n",
    "Or we could do one plot per country. To keep things consistent with the rest of the exercise,\n",
    "let's stick to the latter oprion. So that's settled.\n",
    "\n",
    "But \"top 3 countries\" with respect to what? Two answers suggest themselves:\n",
    "Total sales volume (i.e. total quantity sold) or total sales (i.e. revenue).\n",
    "This exercise goes for sales volume, so let's stick to that.\n",
    "\n",
    "##### Step 7.2.1 Find out the top 3 countries in terms of sales volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Netherlands', 'EIRE', 'Germany'], dtype='object', name='Country')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_volume = online_rt.groupby('Country').Quantity.sum().sort_values(ascending=False)\n",
    "\n",
    "top3 = sales_volume.index[1:4] #We are excluding UK\n",
    "top3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.2.2 \n",
    "\n",
    "Now that we have the top 3 countries, we can focus on the rest of the problem:  \n",
    "\"Quantity per UnitPrice by CustomerID\".  \n",
    "We need to unpack that.\n",
    "\n",
    "\"by CustomerID\" part is easy. That means we're going to be plotting one dot per CustomerID's on our plot. In other words, we're going to be grouping by CustomerID.\n",
    "\n",
    "\"Quantity per UnitPrice\" is trickier. Here's what we know:  \n",
    "*One axis will represent a Quantity assigned to a given customer. This is easy; we can just plot the total  Quantity for each customer.  \n",
    "*The other axis will represent a UnitPrice assigned to a given customer. Remember a single customer can have any number of orders with different prices, so summing up prices isn't quite helpful. Besides it's not quite clear what we mean when we say \"unit price per customer\"; it sounds like price of the customer! A reasonable alternative is that we assign each customer the average amount each has paid per item. So let's settle that question in that manner.\n",
    "\n",
    "#### Step 7.3 Modify, select and plot data\n",
    "##### Step 7.3.1 Add a column to online_rt called `Revenue` calculate the revenue (Quantity * UnitPrice) from each sale.\n",
    "We will use this later to figure out an average price per customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  Revenue  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom    15.30  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom    22.00  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_rt['Revenue'] = online_rt.Quantity * online_rt.UnitPrice\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.2 Group by `CustomerID` and `Country` and find out the average price (`AvgPrice`) each customer spends per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>AvgPrice</th>\n",
       "      <th>Country</th>\n",
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       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12426.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>258</td>\n",
       "      <td>582.73</td>\n",
       "      <td>2.258643</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>533</td>\n",
       "      <td>825.80</td>\n",
       "      <td>1.549343</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>366</td>\n",
       "      <td>729.54</td>\n",
       "      <td>1.993279</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12471.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>8212</td>\n",
       "      <td>19824.05</td>\n",
       "      <td>2.414034</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12472.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>4148</td>\n",
       "      <td>6572.11</td>\n",
       "      <td>1.584405</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity   Revenue  AvgPrice  Country\n",
       "CustomerID Country                                       \n",
       "12426.0    Germany       258    582.73  2.258643  Germany\n",
       "12427.0    Germany       533    825.80  1.549343  Germany\n",
       "12468.0    Germany       366    729.54  1.993279  Germany\n",
       "12471.0    Germany      8212  19824.05  2.414034  Germany\n",
       "12472.0    Germany      4148   6572.11  1.584405  Germany"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = online_rt[online_rt.Country.isin(top3)].groupby(['CustomerID','Country'])\n",
    "\n",
    "plottable = grouped['Quantity','Revenue'].agg('sum')\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# get the value of the index and put in the column Country\n",
    "plottable['Country'] = plottable.index.get_level_values(1)\n",
    "plottable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.3 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################\n",
    "# Graph Section v 2#\n",
    "####################\n",
    "\n",
    "# creates the FaceGrid\n",
    "g = sns.FacetGrid(plottable, col=\"Country\")\n",
    "\n",
    "# map over a make a scatterplot\n",
    "g.map(plt.scatter, \"Quantity\", \"AvgPrice\", alpha=1)\n",
    "\n",
    "# adds legend\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.4 What to do now?\n",
    "We aren't much better-off than what we started with. The data are still extremely scattered around and don't seem quite informative.\n",
    "\n",
    "But we shouldn't despair!\n",
    "There are two things to realize:\n",
    "1) The data seem to be skewed towaards the axes (e.g. we don't have any values where Quantity = 50000 and AvgPrice = 5). So that might suggest a trend.\n",
    "2) We have more data! We've only been looking at the data from 3 different countries and they are plotted on different graphs.\n",
    "\n",
    "So: we should plot the data regardless of `Country` and hopefully see a less scattered graph.\n",
    "\n",
    "##### Step 7.4.1 Plot the data for each `CustomerID` on a single graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = online_rt.groupby(['CustomerID'])\n",
    "plottable = grouped['Quantity','Revenue'].agg('sum')\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# map over a make a scatterplot\n",
    "plt.scatter(plottable.Quantity, plottable.AvgPrice)\n",
    "plt.plot()\n",
    "\n",
    "\n",
    "#Turns out the graph is still extremely skewed towards the axes like an exponential decay function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.4.2 Zoom in so we can see that curve more clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = online_rt.groupby(['CustomerID','Country'])\n",
    "plottable = grouped.agg({'Quantity': 'sum',\n",
    "                         'Revenue': 'sum'})\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# map over a make a scatterplot\n",
    "plt.scatter(plottable.Quantity, plottable.AvgPrice)\n",
    "\n",
    "#Zooming in. (I'm starting the axes from a negative value so that\n",
    "#the dots can be plotted in the graph completely.)\n",
    "plt.xlim(-40,2000) \n",
    "plt.ylim(-1,80)\n",
    "\n",
    "plt.plot()\n",
    "\n",
    "\n",
    "#And there is still that pattern, this time in close-up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot a line chart showing revenue (y) per UnitPrice (x).\n",
    "\n",
    "Did Step 7 give us any insights about the data? Sure! As average price increases, the quantity ordered decreses.  But that's hardly surprising. It would be surprising if that wasn't the case!\n",
    "\n",
    "Nevertheless the rate of drop in quantity is so drastic, it makes me wonder how our revenue changes with respect to item price. It would not be that surprising if it didn't change that much. But it would be interesting to know whether most of our revenue comes from expensive or inexpensive items, and how that relation looks like.\n",
    "\n",
    "That is what we are going to do now.\n",
    "\n",
    "#### 8.1 Group `UnitPrice` by intervals of 1 for prices [0,50), and sum `Quantity` and `Revenue`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UnitPrice\n",
       "(0, 1]    1.107775e+06\n",
       "(1, 2]    2.691765e+06\n",
       "(2, 3]    2.024143e+06\n",
       "(3, 4]    8.651018e+05\n",
       "(4, 5]    1.219377e+06\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the values for the graph.\n",
    "#They are used both in selecting data from\n",
    "#the DataFrame and plotting the data so I've assigned\n",
    "#them to variables to increase consistency and make things easier\n",
    "#when playing with the variables.\n",
    "price_start = 0 \n",
    "price_end = 50\n",
    "price_interval = 1\n",
    "\n",
    "#Creating the buckets to collect the data accordingly\n",
    "buckets = np.arange(price_start,price_end,price_interval)\n",
    "\n",
    "#Select the data and sum\n",
    "revenue_per_price = online_rt.groupby(pd.cut(online_rt.UnitPrice, buckets)).Revenue.sum()\n",
    "revenue_per_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.3 Plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_per_price.plot()\n",
    "plt.xlabel('Unit Price (in intervals of '+str(price_interval)+')')\n",
    "plt.ylabel('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.4 Make it look nicer.\n",
    "x-axis needs values.  \n",
    "y-axis isn't that easy to read; show in terms of millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_per_price.plot()\n",
    "\n",
    "#Place labels\n",
    "plt.xlabel('Unit Price (in buckets of '+str(price_interval)+')') \n",
    "plt.ylabel('Revenue')\n",
    "\n",
    "#Even though the data is bucketed in intervals of 1,\n",
    "#I'll plot ticks a little bit further apart from each other to avoid cluttering.\n",
    "plt.xticks(np.arange(price_start,price_end,3),\n",
    "           np.arange(price_start,price_end,3))\n",
    "plt.yticks([0, 500000, 1000000, 1500000, 2000000, 2500000],\n",
    "           ['0', '$0.5M', '$1M', '$1.5M', '$2M', '$2.5M'])\n",
    "plt.show()\n",
    "\n",
    "#Looks like a major chunk of our revenue comes from items worth $0-$3!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
